adaptive-pricing/INTENT.md

INTENT.md

Project

adaptive-pricing

Purpose

adaptive-pricing exists to provide a practical framework for defining, evaluating, adapting, and implementing pricing models for digital products, services, platforms, and marketplace offerings.

The project starts from the assumption that pricing is not merely an administrative billing concern. Pricing is a central mechanism for discovering product-market fit, expressing value, managing risk, recovering cost, shaping customer behavior, and optimizing long-term business success.

The project therefore treats pricing as an adaptive product capability.

Core Intent

The intent of adaptive-pricing is to build an auto-regulating market value exploring pricing engine.

The engine should help sellers define pricing models that can evolve across the product lifecycle while allowing customers to choose or tune pricing-model parameters within controlled boundaries.

The system should support both:

1. Seller-side optimization Helping vendors, builders, and product owners select pricing models that improve business success, customer lifetime value, contribution margin, adoption, retention, and sustainable growth.

2. Customer-side preference tuning Allowing customers to adjust non-fixed pricing parameters to better match their own preferences, such as lower upfront cost, lower usage cost, stronger predictability, higher flexibility, or better volume economics.

Customer tuning must not create arbitrary discounts. Instead, tunable parameters are solved against seller-defined business goals and boundary conditions.

Problem Space

Pricing strategy requires understanding at least three economic dimensions:

Cost Floor

The minimum economically viable price boundary derived from fixed costs, variable costs, operating cost, support cost, payment-provider fees, capital requirements, risk, and desired margin.

The cost floor answers:

What must we charge to avoid building an unsustainable offering?

Value Range

The range of prices that can reasonably be justified by the value created for different customer segments, use cases, maturity levels, and usage patterns.

The value range answers:

What could this be worth to customers under different conditions?

Market Competition

The externally visible pricing context created by competing products, substitutes, alternative workflows, internal build options, open-source alternatives, manual workarounds, and customer budget expectations.

Market competition answers:

What price can we ask while still being attractive compared with available alternatives?

adaptive-pricing should help sellers reason across these dimensions instead of relying on static guesses.

Product Lifecycle Awareness

Pricing should adapt to the phase of the product lifecycle.

The project should support pricing strategy across the following lifecycle phases:

1. Exploration Pricing is used to learn about willingness to pay, customer value perception, cost structure, and viable business models.

2. Introduction Pricing supports market entry, early adoption, pilot customers, controlled risk, and customer education.

3. Growth Pricing supports scalable acquisition, segmentation, expansion revenue, repeatability, and business-model validation.

4. Maturity Pricing supports margin optimization, packaging, predictable revenue, customer retention, and operational efficiency.

5. Saturation Pricing supports differentiation, bundling, portfolio logic, retention, efficiency, and defense against competition.

6. Decline Pricing supports harvesting, migration, transition offers, reduced investment, long-tail maintenance, or graceful shutdown.

The engine should not assume that one pricing model is appropriate for all phases.

Pricing Model Framework

The project should provide a structured way to define pricing models.

A pricing model may include fixed and non-fixed parameters such as:

• base price
• subscription period
• setup fee
• included usage
• usage unit
• usage price
• usage tiers
• minimum commitment
• contract duration
• volume discounts
• prepayment discount
• risk premium
• support level
• service-level commitment
• cancellation flexibility
• onboarding effort
• customer segment
• feature package
• payment terms
• renewal conditions

Some parameters may be fixed by the seller. Others may be tunable by the customer.

The framework should distinguish clearly between:

• fixed parameters
• seller-controlled parameters
• customer-tunable parameters
• calculated parameters
• constrained parameters
• payment-provider implementation parameters

Customer-Tuned Pricing

Customers should be able to tune non-fixed pricing-model parameters according to their preferences.

Examples:

• lower monthly base price in exchange for higher usage price
• lower usage price in exchange for higher minimum commitment
• lower risk for the seller through upfront payment
• higher flexibility in exchange for a higher price
• volume discounts in exchange for guaranteed minimum turnover
• longer commitment in exchange for more favorable unit economics
• lower onboarding fee in exchange for higher monthly minimum
• stronger predictability through capped usage or prepaid packages

When a customer adjusts one or more pricing parameters, the engine should solve the remaining non-fixed parameters so that the seller’s expected average comparable customer lifetime value improves by a required percentage compared with the most favorable predefined pricing model available to that customer.

This means customer tuning is allowed, but only inside an economically valid solution space.

Comparable Customer Lifetime Value

The engine should use a seller-side comparison metric tentatively called:

average_comparable_customer_lifetime_value

This metric estimates the expected lifetime value of a customer under a given pricing configuration, compared against similar customers, customer segments, usage expectations, risk classes, and contract conditions.

Customer-tuned models should be evaluated against a reference model:

most_favorable_predefined_model

A tuned model is valid only if:

average_comparable_customer_lifetime_value(tuned_model)
>= average_comparable_customer_lifetime_value(most_favorable_predefined_model)
   × required_improvement_factor

The required improvement factor is seller-configurable.

This prevents tuning from becoming unmanaged discounting.

Boundary Conditions

Customer-tuned pricing must be evaluated against explicit boundary conditions.

Boundary conditions may include:

• cost floor
• minimum margin
• payment-provider fees
• expected usage variance
• support effort
• onboarding effort
• contract duration
• churn risk
• default risk
• upfront investment risk
• capital lockup
• implementation effort
• operational capacity
• service-level risk
• abuse risk
• compliance constraints
• tax and accounting constraints
• customer segment eligibility
• sales approval thresholds
• minimum turnover commitment
• maximum discount exposure

The engine should make boundary conditions inspectable and testable.

Volume Discounts

If a pricing model allows volume discounts, those discounts should not be based on optimistic usage assumptions alone.

Volume discounts must be tied to enforceable or economically meaningful commitments, such as:

• minimum monthly turnover
• minimum annual turnover
• prepaid usage packages
• committed usage bands
• longer contract duration
• take-or-pay structures
• reduced cancellation flexibility
• customer-funded onboarding
• guaranteed minimum platform fee

The engine should reject discount structures that reduce seller economics without an offsetting commitment or risk reduction.

Automation Scope

adaptive-pricing should not remain a theoretical pricing strategy document.

The project should provide practical implementation components for:

• pricing-model definition
• pricing-model validation
• pricing simulation
• customer-specific price solving
• price recommendation
• lifecycle-aware pricing strategy
• pricing health checks
• payment-provider synchronization
• customer-facing pricing configuration
• seller-facing pricing governance
• auditability of pricing decisions

The engine should be usable by products and platforms that need to create, update, and manage pricing models automatically.

Payment Provider Integration

A core implementation goal is automated integration with payment providers such as Stripe.

The system should be able to translate internal pricing-model definitions into payment-provider artifacts without requiring manual configuration in the provider dashboard.

For Stripe this may include automated creation and maintenance of:

• products
• prices
• recurring subscriptions
• metered usage prices
• usage records
• tiers
• coupons or discounts where appropriate
• promotion structures where appropriate
• customer-specific subscription configurations
• invoices
• billing periods
• tax-relevant metadata
• product and price metadata
• lifecycle states

The internal pricing model should remain the source of truth.

Payment providers are execution backends, not the primary modeling layer.

Design Principles

Pricing as Discovery

The system should support learning. Especially in early product phases, pricing is a discovery mechanism, not merely a revenue mechanism.

Cost as Boundary, Not Strategy

Cost floor matters, but pricing should not be reduced to cost-plus calculation. The engine should help explore market value above the cost floor.

Value Before Mechanics

Pricing mechanics should serve value communication, adoption, sustainability, and customer success.

Customer Choice Within Seller Safety

Customers may tune pricing parameters, but only within a bounded solution space that protects seller economics.

No Hidden Manual State

Pricing models should be represented explicitly in code and data. Payment-provider state should be generated, synchronized, and auditable.

Lifecycle Awareness

The system should expect pricing to change as the offering moves from exploration to introduction, growth, maturity, saturation, and decline.

Explainability

The engine should explain why a pricing configuration is valid, invalid, recommended, risky, or strategically useful.

Composability

Pricing models should be reusable across products, vendors, tenants, customer segments, and platforms.

Intended Users

The primary users of adaptive-pricing are:

• builders
• vendors
• product owners
• SaaS operators
• marketplace operators
• platform owners
• pricing strategists
• finance operators
• customer success teams
• sales teams
• AI agents assisting with product and pricing decisions

The framework should support both human decision-making and automated agent-driven workflows.

Initial Deliverables

The project should initially produce:

1. A canonical pricing model definition format
2. A pricing strategy vocabulary
3. A pricing lifecycle model
4. A model validator
5. A customer-tuning solver concept
6. A comparable customer lifetime value calculation model
7. Boundary-condition definitions
8. A simulation interface
9. A Stripe integration design
10. A minimal implementation path for automated price creation

Non-Goals

The project is not intended to become only a theoretical pricing research repository.

It is also not intended to hard-code one universal pricing model.

It should not assume that all products are SaaS subscriptions, all services are usage-based, or all customers optimize for the same thing.

It should not treat payment-provider configuration as the source of truth.

Success Criteria

adaptive-pricing is successful when it enables a product team or builder to:

• define pricing models in a structured format
• reason about cost floor, value range, and market competition
• adapt pricing to product lifecycle phase
• expose safe pricing choices to customers
• evaluate customer-tuned pricing against seller economics
• enforce boundary conditions
• connect pricing definitions to Stripe or similar payment providers
• create and update payment-provider pricing artifacts automatically
• simulate expected economic outcomes before activation
• explain why a pricing model is valid, invalid, risky, or recommended

Strategic Direction

adaptive-pricing should become the pricing capability layer for Coulomb offerings and related product platforms.

It should allow pricing to become adaptive, inspectable, customer-aware, seller-safe, and operationally executable.

The long-term ambition is to support pricing as a dynamic control system for product-market fit, customer value exploration, sustainable revenue, and market learning.